Re-Framing Environmental Social Science Research for Sustainable Water Management in a Changing Climate
Bibliographic record
Abstract
Abstract This paper considers aspects of environmental social science research in the UK and explores an obvious bias towards the development of instruments to manage demand as an adaptation to climate change, and consequently the predominance of interest in the customer from a demand-side perspective. In the case of water, this has resulted in an inappropriate mixing of individualist research methods designed to measure public perceptions of risk and water-based practices, with mass consumption data that cannot be specifically linked to the individual. This mixing has a tendency to reinforce a long-standing blame culture that drives interest in the development of behaviour change initiatives while the relatively unchallenged hydraulic mission to provide safe drinking water and sanitation progresses. With this in mind this paper reviews examples of water use research from California, Australia, and the UK and highlights the more effective routes to understanding water customers and developing behaviour change initiatives that utilise stages of change models and grounded techniques incorporating qualitative and quantitative data from individual sources. A secondary aim is to argue for re-framing the relations between various actors in a changing climate to allow the development of new policy approaches, learning, and openness, from industry, regulators, and customers, based on new theories from the field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.078 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.065 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".